With the proliferation of business data in every market sector we see parallel advances in artificial intelligence (AI), machine learning (ML), and general analytics. Successfully getting business value out of these techniques has become a complex affair. This isn’t meant to discourage potential users but really to alert them to potential pitfalls where too many avenues hope to capture customers by offering easy, fast, cheap, and out-of-the-box solutions. Simply put, the complexity of business data and the AI, ML, and analytics techniques needed to squeeze valuable and cost-effective information from it requires an upfront respect for the complexity of the task and a series of techniques and approaches to allow the best chance for successful business implementations of these critical technologies.
At Tessella, we’ve been in the data science and analytics business for nearly forty years – long before these became cool buzzwords. With that experience, we have developed a series of very successful techniques for pulling off the most complex and sophisticated analytics campaigns. We’ve also seen projects that went down roads that proved anywhere from less-than-fully-successful to downright disastrous and we’ve seen many commonalities inherent within these failed approaches.
It’s this composite collection of do’s and don’t’s that I’ll cover in my webinar as lessons learned from real-world success. I’ll begin with the best starting point possible, which is to have a well-defined business problem that you’re trying to get the data to answer for you. This is far more effective than the opposite question of ‘what can the data do,’ which is too open-ended and not likely to generate optimal and cost-effective business answers. After the beginning, I’ll dive into what the underlying nature of AI, ML, and analytics techniques and algorithms actually are. I won’t do this to teach complex mathematics, but to show the underlying complexity and hopefully instill respect for the power of these things to do good – and harm if not properly handled.
Next, with an understanding of what analytics is – and isn’t – I’ll discuss in detail various techniques that we have developed at Tessella that help us achieve consistent success on the widest array of analytics challenges. The key aspect of these techniques is that we’ve learned them by doing and succeeding, and we also see that other successful practitioners are adopting very similar approaches. The upshot of this is, while I can’t prove our approaches are correct, the collective wisdom of success indicates there is something very fundamental here that is best not ignored.
Toward the end of the webinar, I’ll take a look at some of the most common misconceptions folks have with respect to AI, ML, and analytics and the methods employed that regularly lead to failure, wasted time and money, as well as folks updating their resumes. I’m convinced that by showing how analytics is best done and looking at common mistakes the audience can come away with a much better chance of truly extracting business critical information from the flood of data that is hitting every business in every market in this data-inundated world. I hope this webinar enables the audience to get maximum value from their data and to compete effectively with the digital natives bringing innovative data-driven products and services to market.
Join Tessella on a webinar entitled Delivering Business Value from Machine Learning and Analytics on 27th Februuary at 3PM London/10AM New York.